Although the methodology used to estimate the impacts of climate change and the costs of adaptation is specific to each sector, the sectoral methodologies share several elements. Adaptation costs in most sectors were calculated for 2010–50 from a common trajectory of population and GDP growth used to establish the development baseline and a common set of global climate models used to simulate climate effects. For all sectors, adaptation costs include the costs of planned, public policy adaptation measures and exclude the costs of private adaptation. For agriculture, for example, the methodology allows for the effects of autonomous adjustments in the private sector, such as changes in production, consumption, and trade flows in response to world price changes, but does not include the costs of those adjustments in adaptation costs. These common methodological elements, along with wide and in-depth sectoral coverage and a consistent definition of adaptation costs, allow the study to substantially improve on earlier estimates (box 6).
Box 6. Previous estimates of global adaptation costs
World Bank (2006). The first estimate of costs of adaptation to climate change for developing countries was produced by the World Bank in 2006. Its report defined adaptation costs as the cost of climate- proofing three categories of investment flows: official development assistance and concessional finance, foreign direct investment, and gross domestic investment. The study defined the proportion of total investments in each category that was likely to be climate sensitive and then estimated the percentage increases in costs to climate-proof these investments. Adaptation cost estimates ranged from $9 billion to
$41 billion a year.
Stern (2007) and UNDP (2007). Using the same methodology as World Bank (2006) but different values for the proportion of climate-sensitive investments and the increases in costs for climate-proofing
investments, the Stern Report (Stern 2007) estimated costs of adaptation of $4– $37 billion a year by 2050, somewhat lower than the World Bank estimate, while Human Development Report 2007/2008 (UNDP 2007) estimated costs of $5–67 billion a year by 2015, somewhat higher than the World Bank estimate. In addition to the cost of climate-proofing investments, Human Development Report 2007/2008 also estimated that by $40 billion a year would be needed by 2015 to strengthen social protection
programs and scale up aid in other key areas and $2 billion a year to strengthen disaster response systems, boosting overall adaptation costs to $47–109 billion a year by 2015.
Oxfam International (2007). In contrast to this these top-down approaches, Oxfam International (2007) used a bottom-up approach, estimating adaptation costs by assessing National Action Plans for
Adaptation and the costs of adaptation projects initiated by nongovernment organizations. Assuming average warming of 2oC, the report estimated global adaptation costs of at least $50 billion a year: $7.5 billion a year to support adaptation efforts initiated by nongovernmental organizations,1 $8–33 billion a year to meet the costs of the most urgent adaptation measures being proposed under the National Action Plans for Adaptation, and $5–15 billion a year to address unknown and unexpected impacts. Though richer in the range of potential adaptation measures, this methodology uses a small and likely unrepresentative sample of projects and countries to generalize to all developing countries.
UNFCCC (2007). Whereas previous efforts considered only the costs of planned adaptation, the United Nations Framework Convention on Climate Change study considered the costs of both planned and
29
private adaptation measures. Also, whereas previous studies had considered costs across all sectors, this report estimated the costs of adaptation by major sectors (agriculture, forestry, and fisheries; water supply; human health; coastal zones; and infrastructure), yielding total costs of $26–67 billion a year by 2030.
A recent critique of the UNFCCC estimates (Parry and others 2009) suggests that these estimates may be too low because some sectors were excluded (ecosystems, energy, manufacturing, retailing, and tourism), included sectors were not fully accounted for, climate-proofing of infrastructure stocks ignored the need for additional stocks (financed through full funding of development) for handling current climate variability, and residual damages (impacts remaining after adaptation) were not accounted for.
Project Catalyst (2009). The final estimate was produced in 2009 by the Climate Works Foundation’s Project Catalyst initiative. This study estimated that annual average adaptation funding requirements for developing countries lie between $15 billion and $30 billion for the period 2010–20 and between $30 billion and $90 billion by 2030. Softer measures, such as capacity building, planning, and research, are the focus of adaptation policy in the first decade, followed by more expensive structural investments in the second decade. Unlike previous estimates, the study accounts for potential co-benefits of adaptation actions and reduces the cost estimate to reflect these benefits.
C hoosing the timeframe
The choice of timeframe for the analysis of the costs of adapting to climate change will likely affect the overall cost estimates, with a longer timeframe producing higher costs than would a shorter one. The timeframe up to 2050 was selected largely because forecasting climate change and its impacts on an economy becomes even more uncertain beyond this period, and the complexity of the analysis favors getting more precise (or less imprecise) estimates in the near term rather than less precise estimates over a more extended timeline.
Related to the issue of timeframe is the choice of the discount rate, which is related to the timing of investments. The timing of all investments in the sector models is determined by the outcomes of specific climate projections. Given the expected climate outcome within the useful life of an investment, each new investment must be designed to restore welfare (as defined in table 2) to levels that would have existed without climate change. Because of the complexity of modeling sectors at a global level, none of the sectoral models is capable of choosing the optimal timing of investments. This implies that the time-paths of investments is insensitive to changes in the discount rate and therefore all results are presented for a zero discount rate though costs have been expressed in 2005 constant prices. Obviously, discounting the time stream of investment costs would lower the net present value of total investment or adaptation costs, but it would not influence the choice of investments or the underlying investment costs. The inability to model policy tradeoffs across time is a clear limitation imposed by the global nature of this study. The selection of the discount rate and intertemporal choices will be explored in depth in some of the country case studies.
Using baseline G DP and population projections to account for continuing development
Most studies of adaptation to climate change hold developing countries at their current level of development when estimating adaptation costs even over the medium term. Yet most developing
countries will become economically more advanced over the medium term, which will alter the economic
30
impact of climate change and affect the type and extent of adaptation needed. As already explained, the EACC study accounts for the impact of development on estimates of adaptation costs by establishing development baselines by sector (see table 1). These baselines establish a fictional growth path in the absence of climate change that determines sectoral performance indicators, such as stock of infrastructure assets, level of nutrition, and water supply availability. Climate change impacts and costs of adaptation are examined in relation to this baseline.
Baselines are established across sectors using a consistent set of future population and GDP projections.
The population trajectory is aligned with the United Nations Population Division middle-fertility projections for 2006. To ensure consistency with emissions projections, the GDP trajectory is based on the average of the GDP growth projections of the three major integrated assessment models of global emissions growth—Climate Framework for Uncertainty, Negotiation, and Distribution (FUND; Anthoff and Tol 2008); PAGE2002 (Hope 2006); and Regional Dynamic Integrated Model of Climate and the Economy (RICE99; Nordhaus 2001), and growth projections used by the International Energy Agency and the Energy Information Administration of the US Department of Energy to forecast energy demand.
All these sources provide growth estimates at a regionally disaggregated level.
The global average annual real GDP per capita growth rate constructed in this way is 2.1 percent, similar to global growth rates assumed in the United Nations Framework Convention on Climate Change (UNFCCC) A2 emissions scenario from the IPCC 4th Assessment Report (AR4), once considered an extreme scenario but no longer (IPCC 2007). The regionally downscaled GDP projections under different IPCC scenarios (available from the Center for International Earth Science Information Network,
Columbia University) were not used because they are based on older data.
C hoosing climate scenarios and global climate models
Twenty-six global climate models provide climate projections based on the IPCC A2 Special Report on Emission Scenarios (SRES) (see box 7). In this study, the National Center for Atmospheric Research (NCAR) CCSM3 and Commonwealth Scientific and Industrial Research Organization (CISRO) Mk3.0 models were used to model climate change for the analysis of most sectors because they capture a full spread of model predictions to represent inherent uncertainty and they report specific climate variables (minimum and maximum temperature changes) needed for sector analyses. Though the model predictions do not diverge much for projected temperature increases by 2050 (both projecting increases of
approximately 2oC above pre-industrial levels), they vary substantially for precipitation changes. Among the models reporting minimum and maximum temperature changes, the NCAR was the wettest and the CSIRO the driest scenario (globally, not necessarily the wettest and driest in every location) based on the climate moisture index. Climate projections for these two models were created at a 0.5 by 0.5 spatial degree scale and a monthly time scale by applying model predictions through 2050 to a historical climate baseline obtained from the University of East Anglia Climate Research Unit’s Global Climate Database time series 2.1.
Analysis was limited to two specific scenarios rather than the mean multiple of the global climate models because the mean masks extreme values. A model average of near zero could be the result of models predicting near-zero change, but just as well the result of two opposing changes that differ in sign. Using a group of global climate models (multimodel ensembles), as opposed to one model, can somewhat correct for biases and errors. The question with an ensemble approach is how to capture the full range of results from model runs.
31
Box 7. Special Report on Emissions Scenarios of the Intergovernmental Panel on Climate Change Adaptation requires understanding the potential impacts of climate change on human, economic, and ecological systems. Yet attempts to estimate such impacts have to take on a cascade of uncertainty.
Uncertainty starts with the selection of an appropriate underlying emission scenario that is determined by economic and population growth and by energy use choices. Will the world grow rapidly or slowly? Will developing country populations soon adopt the consumption habits of high-income countries? And what kind of energy future are we to look forward to? To account for these questions, the Intergovernmental Panel on Climate Change (IPCC) has developed six socioeconomic scenarios that characterize possible trajectories of emissions.
A scenario is a coherent, internally consistent, plausible description of a possible future state of the world.
It is not a forecast; rather, each scenario is one alternative image of how the future can unfold, given a specific set of assumptions described in a set of four narrative storylines for the climate scenarios: A1 (focus on economic growth and globalization), A2 (regional focus), B1 (environmental focused), and B2 (regional focus). According to the IPCC, all families of scenarios from each storyline are equally valid, with no assigned probabilities of occurrence.
The choice of climate and related nonclimate scenarios is important because it can determine the outcome of a climate impact assessment. According to the IPCC, however, all scenarios have more or less the same projected temperature increase up to 2050 (a timeframe arguably more relevant for adaptation), even though there are large uncertainties regarding carbon dioxide emissions within each scenario. Therefore, the selection of scenarios for this study depends largely on the availability of global climate model data as well as some range of most “likely” future scenarios for the location of interest.
Selecting adaptation measures
Adaptation measures can be classified by the types of economic agent initiating the measure—public or private. The literature distinguishes between autonomous or spontaneous adaptation (adaptation by households and communities acting on their own without public interventions but within an existing public policy framework) and planned adaptation (adaptation that results from a deliberate public policy decision). This study focuses on planned adaptation. This focus is not to imply that autonomous
adaptation is costless. But since the objective is to help governments plan for risks, it is important to have an idea of what problems private markets will solve on their own, how public policies and investments can complement markets, and what measures are needed to protect public assets and vulnerable people.
For that, assessment of planned adaptation is needed.
In all sectors except extreme weather events, “hard” options involving engineering solutions are favored over “soft” options based on policy changes and social capital mobilization (table 3). For adaptation to extreme weather events, the emphasis is on investment in human resources, particularly those of women.
The decision to focus on hard options for the global cost assessment was motivated largely by fact that these are easier to cost. Though hard adaptation options are feasible in nearly all settings, while soft options depend on social and institutional capital, the focus on hard options is not to suggest that they are always preferable. As discussed in box 8, adaptation measures being identified in the companion case studies through participatory scenario workshops span both hard and soft measures. Since hard options are typically more expensive than soft ones, this study assumption is likely to give the estimates an upward bias.
32
Box 8. Adaptation measures identified in participatory workshops
Participants in the participatory scenario development workshop identified several cross-cutting climate change impacts in the infrastructure, natural resource management and agriculture, health and education, land tenure, governance and service delivery, and migration support sectors. Participatory scenario development methods were particularly good at eliciting information on intersectoral linkages among climate impacts and investments and the need for complementary investments. For example, female farmers and others in a local workshop in Kalu, Ethiopia, noted the multiple effects of climate variability on livelihood outcomes in the midland region. They noted that drought and water scarcity led to livestock disease, human health impacts, and reduced household farm productivity and income, resulting in the withdrawal of children from school, distress migration, and more detahs. Calls for adaptation support included investments in watershed management, drought-resistant crop varieties, nonfarm diversification, and capacity building. Local workshop participants in Xai-Xai, Mozambique, highlighted the different income groups within broad sectoral categories (such as commercial producers and nontimber forest collectors within agroforestry) and noted their varied preferences for adaptation investments (see table).
In addition, participants in both workshops identified not only vulnerable populations but also dynamic processes of migration, urbanization, and market development that were leaving some households more vulnerable than others.
Livelihood groups identified in southern Mozambique participatory scenario development workshop
Sector Income tiers Key climate impacts
Select adaptation options sought
Fishing • Commercial fishers
• Artisanal fishers
• Sea level rise,
abandonment of fishing
• Increased salinity in estuaries, reduced fluvial fisheries
• Introduction of new fish species
• Coastal zone pollution reduction measures
Agroforestry • Harvesters (including commercial harvesters)
• Charcoal producers and fuelwood collectors
• Construction pole gatherers
• Nontimber forestry product and food gatherers
• Cyclones, loss of coastal vegetation, ecosystem change
• Floods destroying forest access routes
• Drought, increased physical vulnerability and species change
• Reforestation and dune protection
• Improved road construction planning
• Community involvement and education
Trade and commerce
• Informal and formal sector trading
• Cyclones destroying infrastructure and
• Climate-proof infrastructure;
improved early warning
33
• Differential access to market (seasonal traders, retail traders, wholesale)
displacing people
• Sea level rise, coastal erosion and reduced land for development
systems
• Improved erosion control through public works
Agriculture and ranching
• Large, medium, and subsistence farmers (both rainfed highland farmers and lowland/
floodplain farmers with irrigation)
• Floods and droughts, loss of production, increased livestock disease and death
• Cyclones, loss of lives, crops, infrastructure
• Salinity intrusion
• Barns for animals
• Improved early warning systems
• Better siting of farms
• Dam, floodgate construction Source: Xai-Xai, Mozambique, participatory scenario development workshop report.
In all sectors except extreme weather events, “hard” options involving engineering solutions are favored over “soft” options based on policy changes and social capital mobilization (table 3). For adaptation to extreme weather events, the emphasis is on investment in human resources, particularly those of women.
The decision to focus on hard options for the global cost assessment was largely motivated by fact that these are easier to cost. Though hard adaptation options are feasible in nearly all settings, while soft options depend on social and institutional capital, the focus on hard options is not to suggest that they are always preferable. As discussed in box 8 adaptation measures being identified in the companion case studies through participatory scenario workshops span both hard and soft measures. Since hard options are typically more expensive than soft ones, this study assumption is likely to give the estimates an upward bias.
Table 3. Types of adaptation measures considered, by sector
Sector Adaptation measure
Infrastructure Design standards, climate-proofing maintenance Coastal zones River and sea dikes, beach nourishment, port upgrades Water supply and flood
protection
Reservoir storage, recycling, rainwater harvesting, desalination; flood protection dikes and polders
Agriculture Agricultural research, rural roads, irrigation infrastructure expansion and efficiency improvements
Fisheries Fisheries buybacks, individual transferable quotas, fish farming, livelihood diversification measures, marine protected areas
Human health Prevention and treatment of disease Extreme weather events Investment in human resources
Source: Economics of Adaptation to Climate Change study team.
34
Understanding the limitations of this study
Calculating the cost of adaptation for developing countries requires simplifying a complex problem involving multiple countries, institutions, decisionmakers, and projections of government investments into a world 40 years in the future. This requires constructing projections of economic growth, structural change, climate change, and human behavior over a long time horizon and for numerous sectors. Subject to these constraints, the study has adopted a consistent approach across countries and sectors and over time, establishing a new benchmark for research of this kind.
To do so, however, several important assumptions and simplifications had to be made. The features and limitations of the analysis for each sector are discussed in the sector analyses in section 5. This section looks at five important limitations of the overall study methodology that arise from the need to simplify the problem sufficiently to derive adaptation costs for all developing countries: characterization of
government decisionmaking environment, limited range of climate and growth outcomes, limited scope in time and economic breadth, simplified characterization of human behavior, and top-down versus bottom up analysis.
Stylized characterization of government decision-making environment
The characterization of government decisionmaking is the most problematic element of the study. As have all other attempts to estimate the total costs of adaptation, this study calculates adaptation costs as if decisionmakers knew with certainty what the future climate will be. In truth, current climate knowledge does not permit even probabilistic statements about country-level climate outcomes and therefore provides virtually no help in informing country-level decisionmakers’ investment decisions.4
In fact, with current climate knowledge, country-level decisionmakers face a different problem—how to maximize the flexibility of investment programs to take advantage of new climate knowledge as it becomes available. While this decision problem can be explored at the country level, it is intractable in a global study. Without the assumption of perfect foresight, it would be impossible to calculate adaptation cost for developing countries in all but the most highly stylized and aggregated models. If such an analysis were possible, though, costs of adaptation to climate change would likely be higher than those in this study.
For most durable investment decisions, decisionmakers know with certainty only that climate in the future will differ from climate today. The adaptation costs calculated in this study and in all other global studies are based on the fiction that decisionmakers know what future climate will be and act to prevent its damages.
L imited range of climate and growth outcomes
Even with this strongly stylized characterization of the decision problem, overall model complexity permits systematic exploration of only a small range of potential outcomes. The two major drivers of adaptation costs are climate outcome and economic growth. Of the 26 climate projections available for the A2 SRES, a complete assessment of adaptation costs was possible only with 2. Exploration of
4 Although some researchers have, as a practical expedient, constructed triangular probability densities to represent the range of global climate model outcomes, most climate scientist would object to this use of their data.